Systems and methods for producing personal care products

ABSTRACT

A system, apparatus, and/or method is disclosed for producing a personal care product. An identity of a considered chemical composition may be input into a model (e.g., a machine learning model). The identity of the considered chemical composition may include ingredients. Each of the ingredients of the considered chemical composition may be associated with a value of a chemoinformatic property of chemoinformatic properties of the considered chemical composition. A value of the property of the considered chemical composition may be determined via the model. The value may be based on the identity of the considered chemical composition. The property of the considered chemical composition may be affected by an interaction of at least two of the ingredients of the considered chemical composition. A personal care product comprised of the considered chemical composition may be produced.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 16/731,533, filed Dec. 31, 2019, which are continuationapplications of U.S. patent application Ser. No. 16/672,922, filed Nov.4, 2019, which is a continuation of U.S. patent application Ser. No.16/452,214, filed Jun. 25, 2019, now granted as U.S. Pat. No.10,515,715, the contents of which are all hereby incorporated herein byreference in their entireties.

BACKGROUND

Many products are formed of chemical compositions. Chemical compositionstypically include many different ingredients. Each of the ingredientshave a particular value, such as a chemoinformatic value, associatedwith the ingredient. Further, one or more properties (e.g., a pH, aconsumer perception) of the chemical composition may have unique values,for example, based on the ingredients within the composition. The valueof the property of the chemical composition may change due to theinteraction of the ingredients within the composition.

Conventional methods exist for predicting values of one or moreproperties of a chemical composition. However, such methods are oftentime consuming and nontrivial. For example, conventional approaches topredicting a value of a pH of a chemical composition include (1)experimentally measuring the chemical composition to determine the pHvalue; and (2) performing a mathematical prediction calculation todetermine the pH value (e.g., using known acidity constants, such as pKavalues). These approaches, however, are deficient due to the time and/orcomplexity involved in the respective approaches. Thus, a system and/ormethod is desired that can determine a value of a property of a chemicalcomposition in a way that requires less time and/or less complexity.

BRIEF SUMMARY

A system, apparatus, and/or method is disclosed for producing a personalcare product. An identity of a considered chemical composition may beinput into a model (e.g., a machine learning model). The identity of theconsidered chemical composition may include ingredients. Each of theingredients of the considered chemical composition may be associatedwith a value of a chemoinformatic property of chemoinformatic propertiesof the considered chemical composition. A value of the property of theconsidered chemical composition may be determined via the model. Thevalue may be based on the identity of the considered chemicalcomposition. The property of the considered chemical composition may beaffected by an interaction of at least two of the ingredients of theconsidered chemical composition. A personal care product comprised ofthe considered chemical composition may be produced.

A system, apparatus, and/or method is disclosed for producing a personalcare product. At least one of an identity of a considered chemicalcomposition or values of chemoinformatic properties of ingredients ofthe considered chemical composition may be input into a machine learningmodel. The identity of the considered chemical composition may includeingredients. Each of the ingredients may be associated with a value of achemoinformatic property of chemoinformatic properties of the consideredchemical composition. A fitting parameter value of the consideredchemical composition may be determined, via the machine learning model,based on at least one of an identity of the considered chemicalcomposition or values of chemoinformatic properties of ingredients ofthe considered chemical composition. The fitting parameter value may beassociated with a value of a property of a considered chemicalcomposition. The property of the considered chemical composition may beaffected by an interaction of at least two of the ingredients of theconsidered chemical composition. A personal care product comprised ofthe considered chemical composition may be produced.

A system, apparatus, and/or method is disclosed for producing a personalcare product. At least one of values of chemoinformatic properties ofingredients of the considered chemical composition or a value of aproperty of the considered chemical composition may be input into amodel. The property of the considered chemical composition may beaffected by an interaction of at least two of the ingredients of theconsidered chemical composition. The identity of the considered chemicalcomposition may be determined, via the model, based on the at least oneof the values of the chemoinformatic properties of the ingredients ofthe considered chemical composition or the value of the property of theconsidered chemical composition. A personal care product comprised ofthe identified considered chemical composition may be produced.

In another aspect, a system, apparatus, and/or method is disclosed forproducing a personal care product. Values of chemoinformatic propertiesof ingredients of a sample chemical composition are received. A value ofa property of the sample chemical composition is received in which theproperty is affected by an interaction of at least two of theingredients. The values of the chemoinformatic properties of theingredients of the sample chemical composition and the value of theproperty of the sample chemical composition are input into a model. Anidentity of the considered chemical composition is determined, via themodel, based on at least one of (1) values of chemoinformatic propertiesof ingredients of the considered chemical composition or (2) a value ofa property of the considered chemical composition. The personal careproduct comprised of the considered chemical composition is produced.

In another aspect, a system, apparatus, and/or method is disclosed fordetermining a value of a property of a considered chemical composition.An identity of a sample chemical composition may be received. A samplechemical composition may comprise ingredients. Each of the ingredientsmay be associated with a value of a chemoinformatic property ofchemoinformatic properties of the sample chemical composition. A valueof a property of the sample chemical composition may be received. Theproperty of the sample chemical composition may be affected by aninteraction of at least two of the ingredients of the sample chemicalcomposition. The value of the property of the sample chemicalcomposition and at least one of (1) the identity of the sample chemicalcomposition or (2) the values of the chemoinformatic properties of theingredients of the sample chemical composition may be input into amodel. The value of the property of the considered chemical compositionmay be determined, via the model, based on at least one of (1) anidentity of the considered chemical composition or (2) values ofchemoinformatic properties of ingredients of the considered chemicalcomposition. The property of the considered chemical composition may beaffected by an interaction of at least two of the ingredients of theconsidered chemical composition.

In another aspect, a value of a property of a considered chemicalcomposition may be determined. An identity of a sample chemicalcomposition may be received. The identify may include ingredients. Oneor more (e.g., each) of the ingredients may be associated with a valueof a chemoinformatic property of chemoinformatic properties of thesample chemical composition. A value of a sample physiochemical propertyof the sample chemical composition may be received. The samplephysiochemical property of the sample chemical composition may beaffected by an interaction of at least two of the ingredients of thesample chemical composition. The value of the sample physiochemicalproperty of the sample chemical composition may be input into a model.The identity of the sample chemical composition and/or the values of thechemoinformatic properties of the ingredients of the sample chemicalcomposition may be input into a model. The value of a consideredphysiochemical property of the considered chemical composition may bedetermined via the model. The value may be based on an identity of theconsidered chemical composition and/or values of chemoinformaticproperties of ingredients of the considered chemical composition. Theconsidered physiochemical property of the considered chemicalcomposition may be affected by an interaction of at least two of theingredients of the considered chemical composition. The consideredphysiochemical property may be different than the sample physiochemicalproperty.

In another aspect, a value of a property of a considered chemicalcomposition may be determined. An identity of a characteristic ofinterest may be received. Identities of sample chemical compositions maybe received. Each of the sample chemical compositions may compriseingredients each being associated with a value of a chemoinformaticproperty of chemoinformatic properties of the sample chemicalcomposition. For each of the sample chemical compositions, a value of aproperty that is affected by an interaction of at least two of theingredients of the sample chemical composition may be received. For each(e.g., only each) of the sample chemical compositions having thecharacteristic of interest, the value of the property of the samplechemical composition and at least one of (1) the identity of the samplechemical composition or (2) the values of the chemoinformatic propertiesof the ingredients of the sample chemical composition may be input intoa model. The value of the property of the considered chemicalcomposition may be determined via the model. The value may be based onat least one of (1) an identity of the considered chemical compositionor (2) values of chemoinformatic properties of the ingredients of theconsidered chemical composition. The property of the considered chemicalcomposition may be affected by an interaction of at least twoingredients of the considered chemical composition.

In another aspect, a product comprised of a considered chemicalcomposition having a value of a considered chemical property may beproduced. An identity of a sample chemical composition comprisingingredients may be received. Each of the ingredients may be associatedwith a value of a chemoinformatic property of chemoinformatic propertiesof the sample chemical composition. A value of a training chemicalproperty of the sample chemical composition may be received. The valueof the training chemical property may be based on at least one of anexperimental measurement of the value of the training chemical propertyor a mathematical measurement of the value of the training chemicalproperty. A learning model may be constructed using values of thechemoinformatic properties of the sample chemical composition and thevalue of the training chemical property of the sample chemicalcomposition. The value of the considered chemical property may be inputinto the learning model. Values of chemoinformatic properties of theconsidered chemical composition having the value of the consideredchemical property may be determined via the learning model and/or basedon the value of the considered chemical property. The product comprisingthe considered chemical composition having the value of the consideredchemical property may be produced.

In another aspect, a value of a considered chemical composition havingingredients may be determined. An identity of a sample chemicalcomposition having a defined value of a chemical property may bereceived. The sample chemical composition may be comprised ofingredients that may be different than ingredients of the consideredchemical composition. A training set comprising the identity of thesample chemical composition and the defined value of the chemicalproperty of the sample chemical composition may be generated. A modelfor determining a value of a chemical property of the consideredchemical composition may be constructed based on the training set. Thevalue of the chemical property of the considered chemical compositionmay be determined via the model, for example, based on an identity ofthe considered chemical composition and the training set. The value ofthe chemical property of the considered chemical composition may bereceived.

In another aspect, an identity of a sample chemical compositioncomprising ingredients may be received. Each of the ingredients may beassociated with a value of a chemoinformatic property of chemoinformaticproperties of the sample chemical composition. A value of a fittingparameter associated with a value of a property of the sample chemicalcomposition may be received. The property of the sample chemicalcomposition may be affected by an interaction of at least two of theingredients of the sample chemical composition. The fitting parametervalue associated with the value of the property of the sample chemicalcomposition and/or at least one of (1) the identity of the samplechemical composition or (2) the values of the chemoinformatic propertiesof the ingredients of the sample chemical composition may be input intoa model. A fitting parameter value of the considered chemicalcomposition may be determined via the model. The fitting parameter valuemay be based on at least one of (1) an identity of the consideredchemical composition or (2) values of chemoinformatic properties ofingredients of the considered chemical composition.

In another aspect, a considered chemical composition may be identified.Values of chemoinformatic properties of ingredients of a sample chemicalcomposition may be received. A value of a property of the samplechemical composition may be received. The property may be affected by aninteraction of at least two of the ingredients. The values of thechemoinformatic properties of the ingredients of the sample chemicalcomposition and the value of the property of the sample chemicalcomposition may be input into a model. An identity of the consideredchemical composition may be determined via the model, for example, basedon at least one of (1) values of chemoinformatic properties ofingredients of the considered chemical composition or (2) a value of aproperty of the considered chemical composition. The property of theconsidered chemical composition may be affected by an interaction of atleast two of the ingredients of the considered chemical composition.

In another aspect, identities of components of a first composition maybe received. Each of the components may have a value of a predefinedcharacteristic of predefined characteristics. A value of a property ofthe first composition may be received. The property may be affected byan interaction of at least two of the components of the firstcomposition. A learning model may be trained using the values of thepredefined characteristics of the components of the first compositionand the value of the property of the first composition. Identities ofsecond components of a second composition may be provided to thelearning model. At least one of the second components may be differentthan at least one of the first components. A value of a property of thesecond composition may be determined via the learning model. Theproperty of the second composition may be affected by an interaction ofat least two of the components of the second composition.

In another aspect, a value of a property of a considered chemicalcomposition may be determined. An identity of the considered chemicalcomposition from chemical compositions associated with a personal careproduct may be received. The considered chemical composition may includeingredients. Values of chemoinformatic properties may be received. Eachvalue may be associated with a respective one of the ingredients of theconsidered chemical composition. The value of the property of theconsidered chemical composition may be determined based on at least oneof (1) the identity of the considered chemical composition or (2) thevalues of the chemoinformatic properties associated with the respectiveone of the ingredients of the considered chemical composition. The modelmay be trained by at least one of (1) an identity of a chemicalcomposition or (2) values of chemoinformatic properties of ingredientsof the chemical composition with a value of a property of the chemicalcomposition. The value of the property of the considered chemicalcomposition may be affected by an interaction of at least two of theingredients of the considered chemical composition.

In another aspect, a model may be created to determine a value of aproperty of a considered chemical composition. An identity of a samplechemical composition comprising ingredients may be received. Each of theingredients may be associated with a value of a chemoinformatic propertyof chemoinformatic properties of the sample chemical composition. Avalue of a property of the sample composition may be received. Theproperty may be affected by an interaction of at least two of theingredients of the sample chemical composition. A model may be trainedto determine the value of the property of the considered chemicalcomposition by processing the value of the property of the samplechemical composition and at least one of (1) an identity of the samplecomposition or (2) the values of the chemoinformatic properties of theingredients of the sample chemical composition. The model may beconfigured to determine the value of the property of the consideredchemical composition based on at least one of (1) an identity of theconsidered chemical composition or (2) values of chemoinformaticproperties of ingredients of the considered chemical composition.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more fully understood from thedetailed description and the accompanying drawings, wherein:

FIG. 1A is a table of ingredients of an exemplary composition;

FIG. 1B is a table of example properties of composition in whichconsumers may have a perception;

FIG. 2A is a block diagram of example components of a composition, thecomponents including ingredients and substances of the composition;

FIG. 2B is a table of ingredients of another exemplary composition,providing identities of the ingredients and percentages of theingredients;

FIG. 3A is an example process for determining a value of a compositionusing machine learning rules;

FIG. 3B is an exemplary system for determining a value of a composition;

FIG. 4 is another example process for determining a value of acomposition using machine learning rules;

FIG. 5 is a block diagram of an example system including a user device;

FIG. 6 is a block diagram of an example system including a training of aproperty engine;

FIG. 7 is a table of example functions of ingredients of a composition;

FIG. 8 is a table of example classifications of ingredients of anexemplary composition;

FIGS. 9A, 9B, 9C are block diagrams of an example training of a machinelearning model and receiving values from the machine learning model;

FIGS. 10A, 10B, 10C are example graphical user interfaces (GUIs) fortraining a property engine;

FIGS. 11A, 11B, 11C, 11D are example graphical user interfaces (GUIs)for receiving a determined value via a property engine; and

FIG. 12 is an example method of determining values of a composition, asdescribed herein.

DETAILED DESCRIPTION

The following description of the preferred embodiment(s) is merelyexemplary in nature and is in no way intended to limit the invention orinventions. The description of illustrative embodiments is intended tobe read in connection with the accompanying drawings, which are to beconsidered part of the entire written description. In the description ofthe exemplary embodiments disclosed herein, any reference to directionor orientation is merely intended for convenience of description and isnot intended in any way to limit the scope of the present invention. Thediscussion herein describes and illustrates some possible non-limitingcombinations of features that may exist alone or in other combinationsof features. Furthermore, as used herein, the term “or” is to beinterpreted as a logical operator that results in true whenever one ormore of its operands are true. Furthermore, as used herein, the phrase“based on” is to be interpreted as meaning “based at least in part on,”and therefore is not limited to an interpretation of “based entirelyon.”

As used throughout, ranges are used as shorthand for describing each andevery value that is within the range. Any value within the range can beselected as the terminus of the range. In addition, all references citedherein are hereby incorporated by referenced in their entireties. In theevent of a conflict in a definition in the present disclosure and thatof a cited reference, the present disclosure controls.

Features of the present invention may be implemented in software,hardware, firmware, or combinations thereof. The computer programsdescribed herein are not limited to any particular embodiment, and maybe implemented in an operating system, application program, foregroundor background processes, driver, or any combination thereof. Thecomputer programs may be executed on a single computer or serverprocessor or multiple computer or server processors.

Processors described herein may be any central processing unit (CPU),microprocessor, micro-controller, computational, or programmable deviceor circuit configured for executing computer program instructions (e.g.,code). Various processors may be embodied in computer and/or serverhardware of any suitable type (e.g., desktop, laptop, notebook, tablets,cellular phones, etc.) and may include all the usual ancillarycomponents necessary to form a functional data processing deviceincluding without limitation a bus, software and data storage such asvolatile and non-volatile memory, input/output devices, graphical userinterfaces (GUIs), removable data storage, and wired and/or wirelesscommunication interface devices including Wi-Fi, Bluetooth, LAN, etc.

Computer-executable instructions or programs (e.g., software or code)and data described herein may be programmed into and tangibly embodiedin a non-transitory computer-readable medium that is accessible to andretrievable by a respective processor as described herein whichconfigures and directs the processor to perform the desired functionsand processes by executing the instructions encoded in the medium. Adevice embodying a programmable processor configured to suchnon-transitory computer-executable instructions or programs may bereferred to as a “programmable device”, or “device”, and multipleprogrammable devices in mutual communication may be referred to as a“programmable system.” It should be noted that non-transitory“computer-readable medium” as described herein may include, withoutlimitation, any suitable volatile or non-volatile memory includingrandom access memory (RAM) and various types thereof, read-only memory(ROM) and various types thereof, USB flash memory, and magnetic oroptical data storage devices (e.g., internal/external hard disks, floppydiscs, magnetic tape CD-ROM, DVD-ROM, optical disk, ZIP™ drive, Blu-raydisk, and others), which may be written to and/or read by a processoroperably connected to the medium.

In certain embodiments, the present invention may be embodied in theform of computer-implemented processes and apparatuses such asprocessor-based data processing and communication systems or computersystems for practicing those processes. The present invention may alsobe embodied in the form of software or computer program code embodied ina non-transitory computer-readable storage medium, which when loadedinto and executed by the data processing and communications systems orcomputer systems, the computer program code segments configure theprocessor to create specific logic circuits configured for implementingthe processes.

Compositions may include one or more ingredients. For example, acomposition may include a first, second, third, etc. ingredient. One ormore of the ingredients of a composition may have an effect on one ormore other ingredients of the composition. Also, or alternatively, oneor more of the ingredients may have an effect on the composition (e.g.,the composition as a whole).

A composition may be a chemical composition. The chemical compositionmay form a product. The chemical composition (e.g., product formed fromthe chemical composition) may be used for one or more purposes. Forexample, a product formed from a chemical composition may be used forcooking; cleaning; personal care; treating/testing for diseases,disorders, conditions; as well as one or more other purposes. Acomposition (e.g., chemical composition) may be used for performingtasks. For example, a chemical composition may be used for performingtests, such as water purity tests.

A chemical composition may form a personal care product, although apersonal care product is for illustration purposes only and a chemicalcomposition may form one or more other products. A personal care productmay exist for enhancing a user's health, hygiene, appearance, etc. Suchpersonal care products may comprise one or more chemical compositionsthat are comprised of one or more ingredients. Personal care productsmay include oral care products comprising oral care compositions, skincare products comprising skin care compositions, hair care productscomprising hair care compositions, as well as other products and/orchemical compositions.

Oral care composition, as used herein, may include a composition forwhich the intended use can include oral care, oral hygiene, oralappearance, or for which the intended use may comprise administration tothe oral cavity. Skin care composition, as used herein, may include acomposition for which the intended use may include promotion orimprovement of health, cleanliness, odor, appearance, and/orattractiveness of skin. Hair care compositions, as used herein, mayinclude a composition for which the intended use may include promotionor improvement of health, cleanliness, appearance, and/or attractivenessof hair. The compositions may be used for a wide variety of purposes,including for enhancing personal health, hygiene, and appearance, aswell as for preventing or treating a variety of diseases and otherconditions in humans and in animals.

FIG. 1A shows a table of data associated with an example composition.The composition may be a chemical composition, such as chemicalcomposition 100. Chemical composition 100 may form a product, such as apersonal care product. As can be seen from FIG. 1A, chemical composition100 may include several ingredients. For example, chemical composition100 may include glycerin, sodium lauryl sulfate, zinc citrate, as wellas one or more other ingredients. Each of the ingredients of thechemical composition 100 (e.g., the chemical composition forming apersonal care product) may be included in the personal care product toprovide one or more predefined features. As provided in FIG. 1A,features of the ingredients may include providing sweetness to thechemical composition 100, providing stabilizing factors to the chemicalcomposition 100, etc. For example, sodium lauryl sulfate is aningredient of the chemical composition 100 that may be used as asolubilizing or cleansing agent for the chemical composition 100.

FIG. 1B shows a table of additional data associated with an examplecomposition, such as chemical composition 100. In an example thechemical composition may form a product, such as a personal careproduct. The data provided in FIG. 1B may relate to a consumerperception of a personal care product. As can be seen from FIG. 1B, aconsumer may perceive a chemical composition according to severalcategories and/or features. For example, consumers may have perceptionsabout the color, stickiness, wetness, ease of use, sweetness, etc., of achemical composition. A consumer may have a preference for a personalcare product based on the perception of one or more features of thechemical composition forming the personal care product. For example, aconsumer may prefer that a toothpaste have a certain color, that ashampoo have a certain smell, that a deodorant have a certaindispersibility, etc. One or more consumers may rate a personal careproduct based on one or more perception values that the consumer hasregarding the personal care product. The values of consumer perceptionsmay be obtained in a variety of ways, including a survey (e.g., a paperor online survey), a clinical trial, commercial success of the personalcare product, etc.

Consumer perceptions of a chemical composition (e.g., a chemicalcomposition forming a personal care product) may be based on one or moreof the ingredients of the personal care product. Said another way, aconsumer perception value may be affected by one or more ingredients ofthe personal care product. For example, a particular ingredient maycause a personal care product to be more white or less white, to be moresticky or less sticky, to cause more of a burning sensation or less of aburning sensation, etc.

FIG. 2A shows a depiction of data associated with a composition (e.g., achemical composition). The composition may form a product, such as apersonal care product 200. For example, data associated with personalcare product 200 may include an identity, such as a name of the personalcare product, ingredients of the personal care product, chemoinformaticvalues of ingredients of personal care product, or another identifierused to identify the personal care product. For example, personal careproduct 200 may be associated with a unique number 212 a that may bereferenced by a user and/or computer when referring to the personal careproduct. Each personal care product 200 may have one or more othervalues and/or properties, such as property 212 b. Property 212 b may bea chemical property associated with the chemical composition of thepersonal care product. For example, property 212 b may be aphysiochemical property of the chemical composition of the personal careproduct. The physiochemical property of the chemical composition mayrelate to a physical property or a chemical property of the chemicalcomposition of the personal care product. For example, property 212 bmay be a pH value of the personal care product. The value of property212 b may be affected by one or more ingredients of the personal careproduct.

Property 212 b may be a consumer perception of the personal careproduct, such as the perceptions shown in FIG. 1B. For example,consumers may have perceptions about the color, stickiness, wetness,ease of use, sweetness, etc., of the personal care product. The consumerperceptions of the personal care product may be based on one or more ofthe ingredients of the personal care product. For example, an ingredientmay cause a personal care product to be more white or less white, to bemore sticky or less sticky, to cause more of a burning sensation or lessof a burning sensation, etc.

As described herein, a chemical composition may form a personal careproduct. The chemical composition may be comprised of one or moreingredients (e.g., ingredient data), such as ingredients 222, 232. Eachingredient may include an identity, such as a name of the ingredient orother identifier used to identify the ingredient. For example,ingredient 222 may include a name 222 b and/or an identifier 222 a.Ingredient 222 may include other information, such as the percentage ofthe personal care product that comprises the ingredient. For example, asshown in 222 c, ingredient 222 (e.g., sodium lauryl sulfate) may be1.4999% of the personal care product 200. Ingredient data may includeone or more other properties and/or values of the properties.

Each ingredient may further be made up of one or more substances. Asshown in FIG. 2A, ingredient 222 may be comprised of four substances:water 242, sodium sulfate 252, sodium chloride 262, and sodium C12-16Alkyl Sulfate 272. Data may be associated with one or more (e.g., each)of the substances. For example, each substance may include an identity,such as a name 242 a, 252 a, 262 a, 272 a of the substance or otheridentifier used to identify the substance. As an example, substance 242may have a name 242 a of water. The substance (such as substance 242,252, 262, 272) may include one or more other values, such as thepercentage 242 b that the substance makes up of the ingredient,chemoinformatic properties of the ingredients (e.g., substances of theingredients), etc. For example, substance 242 (i.e., water) may comprise70% of the ingredient 222 sodium lauryl sulfate, as shown on 242 b.Substance 272 may include chemoinformatic properties such as a chemicalclass, an HLB value, a surface area (e.g., topological polar surfacearea), etc. As shown on FIG. 2A, example information may include achemical class of Alkyl Sulfate, an HLB value of 40, and/or atopological polar surface area of 74.8 squared Angstroms. In otherexamples, however, one or more substances may have one or more (e.g.,different) chemoinformatic properties having one or more differentvalues.

As described herein, property 212 b of personal care product may beaffected by interactions of one or more of the ingredients of thechemical composition that is the personal care product 200. Examples ofproperty 212 b may relate to a pH, fluoride (e.g., fluoride stability),viscosity (e.g., viscosity stability), viscoelasticity, abrasion (e.g.,stain removal and dentin abrasion), color, turbidity, analyteconcentration, specific gravity, consumer perception (e.g., sweetness,stickiness, fragrance), etc., of a personal care product.

The value of property 212 b of the chemical composition of the personalcare product may be determined by experimentally measuring the value ofthe property. By experimentally measuring the property of the personalcare product, the actual value of the property may be determined. Thevalue of the property 212 b may be determined via a mathematical (e.g.,thermodynamic) calculation of the value of the property. For example, adatabase of personal care product compositions may be compiled. Thecompositions may include one or more compositions. A catalogue (e.g., ahand-evaluated catalogue) may contain one or more constants (e.g., metalbinding constants, surface acidity constant, etc.), and/or one or moresolubility products, for example. Speciation calculations may beperformed on personal care product compositions. The speciationcalculations may be used to determine the activity of one or more (e.g.,each) ion of a personal care product composition. The negative log ofthe hydrogen ion may correspond (e.g., activity correspond) to acalculated value (e.g., the calculated pH value) of the personal careproduct composition.

The value of the property 212 b may be determined by receiving consumerperceptions of one or more attributes of the personal care product. Forexample, clinical consumer trials may be used to determine consumerperceptions about the personal care product. The clinical consumertrials may determine how consumers (e.g., potential consumers) perceivethe personal care product. For example, clinical consumer trials maydetermine how consumers feel about the color (e.g., whiteness),stickiness, wetness, sweetness, fragrance, bitterness, ease of use, etc.of the personal care product. Consumer perceptions may be determined viaother methods, including surveys (e.g., online and paper surveys),commercial success, etc. FIG. 1B provides a list of example propertiesof personal care product in which consumers may have a perception.

FIG. 2B is an example table of data relating to a composition. Thecomposition may be chemical composition that may form a personal careproduct. As described herein, examples of personal care products mayinclude oral care products (e.g., a toothpaste, mouthwash, etc.), hairproducts (e.g., a shampoo, hair gel, etc.), skin products (e.g.,moisturizers, soaps, etc.), etc. A personal care product may includeingredients, such as the example ingredients named under column 282. Forexample, the chemical composition forming the personal care product mayinclude ingredients such as sorbitol, water, glycol, etc.

The ingredients (e.g., each of the ingredients) may be identified in oneor more ways. For example, the ingredients may be identified by name.Also, or alternatively, the ingredients may be identified by anidentification number (e.g., a unique identification number), such as bythe identification numbers provided under column 280. The identificationnumber may be used by a user and/or one or more software applications toidentify the ingredient. The identification number may be used toconceal the true identity of the ingredients, for example, in instanceswhen the identification of the ingredients is confidential. Theidentification numbers may be randomly generated, may be generatedand/or listed in an order (such as an increasing or decreasing order),etc. Although the table of FIG. 2B shows the identifications undercolumn 280 as alphanumeric characters, it will be understood by those ofskill in the art that the identifications may be represented as anycombination of numbers, letters, special characters, etc.

FIG. 2B further provides values, such as the percentage values shown incolumn 284. The percentage values may relate to the weight percentage ofthe ingredient in the composition (e.g., a personal care product). Forexample, as shown in FIG. 2B, water may comprise 10 to 30 percent of thecomposition, and sodium saccharin may comprise 0.1 to 1 percent of thetotal weight of the composition.

As described herein, personal care products may be formed of (e.g.,formulated using) one or more chemical compositions comprising one ormore ingredients. Formulating personal care products using more than onechemical composition and/or one or more ingredients may present a numberof challenges. For example, combining chemical compositions may causevalues of properties of the chemical composition forming the personalcare product to change. As an example, combining two or more ingredientsin a chemical composition may cause the pH value to change. The pH valuemay be changed in an unpredictable way, for example, based on theinteraction of the two or more ingredients.

As adding, removing, and/or mixing ingredients within a chemicalcomposition may affect values of properties of the chemical composition,it may be difficult to create personal care products in which theaddition, reduction, or mixing of ingredients is required. For example,personal care products may be required to be pharmaceutically and/orcosmetically acceptable for their intended uses and/or purposes. Theintended uses and/or purposes may be based on a value of a property(e.g., pH) of the chemical composition. By combining new ingredients toa chemical composition, or removing ingredients from a chemicalcomposition, a value of the property (e.g., a value of pH) of thechemical composition may change such that the chemical compositionforming the personal care product is no longer suitable for the personalcare products intended purposes.

Chemical compositions forming personal care products may containtherapeutic active materials that may (e.g., may only) deliver desiredresults if the compositions have not exhibited a chemical degradation.By combining new ingredients to a chemical composition, or removingingredients from a chemical composition, a value of the property of thechemical composition may change such that the chemical compositionforming the personal care product incurs a chemical degradation. Such achemical degradation may cause the personal care product to no longer besuitable for consumer use.

Chemical compositions forming personal care products may containcosmetically functional materials that may (e.g., may only) deliver thematerial to the oral cavity, skin, and/or hair, etc. at effective levelsunder the conditions that they are typically used by the consumer. Bycombining new ingredients to a chemical composition, or removingingredients from a chemical composition, a value of the property of thechemical composition may change such that the chemical compositionforming the personal care product no longer performs at the requiredeffective levels.

Chemical compositions forming personal care products may (e.g., mayonly) exhibit an aesthetic appearance for a time period. Such aestheticappeal of chemical compositions may be important, for example, as suchaesthetic appeal may have significant effects on consumer acceptance andusage. By combining new ingredients to a chemical composition, orremoving ingredients from a chemical composition, a value of theproperty of the chemical composition may change such that the chemicalcomposition forming the personal care product is no longer aestheticallypleasing.

Chemical compositions forming personal care products may exhibit one ormore attributes that are perceived by a consumer. For example, chemicalcompositions forming a personal care product may exhibit a flavor,sweetness, ease of use, etc., as perceived by a consumer. By combiningnew ingredients to a chemical composition, or removing ingredients froma chemical composition, a value of the property of the chemicalcomposition may change such that the chemical composition forming thepersonal care product affects the consumer perception of the personalcare product. For example, the value of the property of the chemicalcomposition may be affected such that the personal care product exhibitsa more minty flavor, a more salty taste, etc.

As described herein, it may be possible to determine a value of aproperty of a chemical composition forming a personal care product. Forexample, a value of a property of a personal care product compositionmay be experimentally measured, mathematically calculated, and/orreceived via clinical consumer trials. However, such techniques may betime consuming, nontrivial, and/or impossible, as the personal careproduct composition may include dozens (or more) of ingredients. Machinelearning techniques may be used to determine one or more values ofproperties of a chemical composition.

FIG. 3A shows an example process 300 for using machine learningtechniques to determine (e.g., predict) an attribute. The attribute mayinclude an identity of a composition, ingredients of a composition, avalue of a property of a composition, etc. For example, the attributemay include an identity of a chemical composition forming a personalcare product, ingredients of a chemical composition forming a personalcare product, a value of a property (e.g., a pH value, a fluoridestability value, a viscosity value, an abrasion value, a specificgravity value, a consumer perception value) of a chemical compositionforming a personal care product, etc. Although the disclosure maydescribe the determination (e.g., prediction) of an identity of achemical composition forming a product (e.g., personal care product) ora value of a property of the chemical composition forming the product(e.g., personal care product), it should be understood that machinelearning techniques may also, or alternatively, be used to determine(e.g., predict) other values, such as other identities of products,values of chemoinformatic properties of ingredients of products, etc.

At 302, one or more identities of chemical compositions (e.g., samplechemical compositions of a personal care product) may be stored, forexample, in a database. The identity of the chemical composition mayinclude a name of a chemical composition, ingredients of the chemicalcomposition (e.g., formulations of the chemical composition), etc. Forexample, as shown in FIG. 2A, the identity of the chemical compositionmay include chemoinformatic properties (e.g., chemoinformatic values) ofeach of the ingredients of the chemical composition. The identity of achemical composition may be received from one or more of the databases.

At 303, one or more perceptions of chemical compositions forming one ormore products (e.g., personal care products) may be determined and/orreceived. The perceptions of the chemical compositions may be determinedand/or identified via consumers (e.g., potential consumers). Theperceptions may be determined and/or identified via clinical consumertrials, for example. The perceptions may include the whiteness of thepersonal care product, how minty the personal care product is, thesweetness of the personal care product, etc. The perceptions of thechemical compositions may be affected by one or more ingredients of thechemical composition forming the personal care product. For example, oneor more ingredients may affect how minty a consumer perceives thepersonal care product to be, how sweet the consumer perceives thepersonal are product to be, how white the consumer perceives thepersonal are product to be, etc.

At 304, values of properties of chemical compositions forming one ormore products (e.g., personal care products) may be determined viaexperimental measurements. The values of the properties may be affectedby one or more ingredients of the chemical composition. Theexperimentally measured values of properties of the chemical compositionmay be identified by performing actual measurements of the values of theproperties of the chemical compositions. The experimentally measuredvalues of properties of the chemical composition may be identified byretrieving the experimentally measured values of the properties from adatabase, for example, after the experimentally measured values of theproperties have been stored in a database. The experimentally measuredvalues of properties of the chemical composition (e.g., sample chemicalcompositions) may be received.

At 306, one or more values of a chemical composition forming a personalcare product may be determined and/or stored, for example, in adatabase. The one or more values of the chemical composition may relateto physiochemical properties of a chemical composition. The value of aphysiochemical property may include a value for one or more (e.g., each)ingredients of the chemical composition. The value of a physiochemicalproperty may be received, for example, from one or more databases.

At 308, the values of physiochemical properties of the chemicalcomposition may be identified and/or determined. The values ofphysiochemical properties of the chemical composition may be determinedby measuring the physiochemical properties of the ingredients of thechemical composition, calculating (e.g., mathematically calculating)predicted values of the physiochemical properties of the chemicalcompositions, looking up the values of the physiochemical properties(e.g., via a database, look-up table, etc.), etc. The values ofphysiochemical properties of the chemical composition may be identifiedand/or determined via thermodynamic calculations of the physiochemicalproperties.

At 310, data may be input into a machine learning model, as describedherein. For example, identities of chemical compositions may be inputinto the model. Identities of chemical compositions may include names ofone or more of the chemical compositions, identities of ingredients ofthe chemical compositions, values of chemoinformatic properties (ofingredients) of the chemical compositions, etc. Values of properties ofthe chemical composition may be input into the machine learning model.For example, values of properties (e.g., experimentally measured values,mathematically calculated values, consumer perceived values) of thechemical composition may be input into the model. Data related tochemical compositions may be input into the model to train the model, inexamples. In other examples, data related to chemical compositions maybe input into the model to determine values (e.g., other values) of thechemical compositions.

An association may be input into the model. For example, there may be anassociation between an identity (e.g., ingredients) of a chemicalcomposition and a value of a property of the chemical composition. Theingredients of a chemical composition (e.g., a sample chemicalcomposition) and the associated value of the property of the chemicalcomposition (e.g., a sample chemical composition) may be input into amachine learning model, for example, to train the machine learningmodel.

At 312, the machine learning model may determine (e.g., predict) a valueof one or more pieces of data relating to the chemical composition. Forexample, if an identity of a chemical composition (e.g., a consideredchemical composition) is input into the machine learning model, themachine learning model may determine (e.g., predict) a value of aproperty of the chemical composition based on the identity of thechemical composition. Conversely, if a value of a property of a chemicalcomposition (e.g., considered chemical composition) is input into themachine learning model, the machine learning model may determine (e.g.,predict) an identity of the chemical composition based on the value ofthe property of the chemical composition.

FIG. 3B is an example diagram of system 350 for determining informationrelating to a composition, such as a chemical composition forming aproduct (e.g., a personal care product). The information may relate toan identity of a chemical composition, chemoinformatic values of thechemical composition, as well as one or more other values relating to aproduct. System 350 may be a data warehouse, in an example. For example,system 350 may include one or more databases for receiving, storing,and/or providing data and/or one or more processors for processing thedata received, stored, and/or provided by one or more of the databases.

System 350 may include element 352, which may include one or moredatabases. For example, element 352 may include one or more databasesreceiving, storing, and/or providing formulation identifiers, rawmaterials in one or more (e.g., each) of the formulations, and/or weightpercentages of one or more (e.g., each) of the raw materials in aformulation. Element 352 may include one or more databases receiving,storing, and/or providing formulation identifiers, descriptive sales,and/or logistical information. Element 352 may include one or moredatabases receiving, storing, and/or providing raw material identifiers,cost(s), manufacturer information, and/or logistical information.Element 352 may include one or more databases receiving, storing, and/orproviding raw material identifiers, chemicals in one or more (e.g.,each) raw material, and/or weight percentages of one or more (e.g.,each) chemical in a raw material. Element 352 may include one or moredatabases receiving, storing, and/or providing raw material identifiersand/or informatic (e.g., chemoinformatic) properties of the rawmaterials. Element 352 may include one or more databases receiving,storing, and/or providing chemical identifiers and/or informatic (e.g.,chemoinformatic) properties of the chemicals. Element 352 may includeone or more databases receiving, storing, and/or providing thermodynamicand kinetic reaction constants between chemicals, such as all knownthermodynamic and kinetic reaction constants between all chemicals.

At 354, feature selection, representation, and/or engineering may beperformed. For example, rules (e.g., algorithms) may perform featureselection, representation, and/or engineering.

System 350 may include element 356, which may include one or moredatabases. For example, element 356 may include one or more databasesreceiving, storing, and/or providing formulation identifiers, selectfeatures in a (e.g., each) formulation (such as a combination ofidentifiers, material informatics, chemical informatics, etc.), and/orrepresentation (e.g., quantitative representation) of the abundance of afeature in a formulation.

At 362, chemical speciation calculations (e.g., based on thermodynamicand/or kinetic constants) may be performed. For example, rules (e.g.,algorithms) may perform chemical speciation calculations (e.g., based onthermodynamic and/or kinetic constants).

System 350 may include element 364, which may include one or moredatabases. For example, element 364 may include one or more databasesreceiving, storing, and/or providing formulation identifiers, calculatedvalues of a property of a chemical composition, and/or equilibriumproperties (e.g., based on kinetics and thermodynamic constants).

System 350 may include element 366, which may include one or moredatabases. For example, element 366 may include one or more databasesreceiving, storing, and/or providing formulation identifiers and/ortesting values (e.g., experimentally determined analytical testingvalues of a property of a chemical composition). The property of thesample chemical composition may be affected by an interaction two ormore of the ingredients of the sample chemical composition. Element 366may include one or more databases receiving, storing, and/or providingformulation identifiers and/or consumer-derived testing results. Element366 may include one or more databases receiving, storing, and/orproviding formulation identifiers and/or clinical testing results.

At 368, fitting parameters of testing results may be determined. Forexample, rules (e.g., algorithms) may determine fitting parameters oftesting results.

System 350 may include element 370, which may include one or moredatabases. For example, element 370 may include one or more databasesreceiving, storing, and/or providing formulation identifiers, aggregatedtesting results, and/or fitting parameters associated with testingresults.

At 358, machine learning information may be determined. For example,rules (e.g., algorithms) may determine machine learning information.

System 350 may include element 360, which may include one or moredatabases. For example, element 360 may include one or more databasesreceiving, storing, and/or providing machine learning model parameters.

FIG. 4 is a process 400 showing other example steps of predictingchemical composition information via machine learning rules, asdescribed herein. Hatch lines are used in FIG. 4 to denote relationshipswithin the process.

At 402, the process begins. At 404, an entity (e.g., a business) maybegin to understand and/or improve its understanding of chemicalcomposition information. For example, the entity may begin and/orimprove its understanding of a need for chemical compositions to have afeature, such as a chemical composition having a pH of a certain value,an emulsifying purpose, a sweetness, a thickener, etc. Although theentity may understand a need for the chemical composition to have acertain feature (e.g., value), the entity may not know the ingredientsof the chemical composition that will create such a feature (e.g.,value).

At 406, data relating to the chemical composition may be acquired. Forexample, the entity may acquire identities (e.g., names, ingredients,chemoinformatic properties, etc.) of chemical compositions, values ofproperties of chemical compositions, etc. The information of thechemical composition may be acquired via an experimental measurement, amathematical computation, clinical consumer trials, one or more datasources (e.g., a database, file, etc.), or other informational avenues.An association between the information may be identified and/ordetermined. For example, an association between an identity of achemical composition and a value of a property of the chemicalcomposition may be determined.

At 408, a machine learning model may be trained and/or used, asdescribed herein. For example, information relating to a chemicalcomposition (e.g., a sample chemical composition) may be used to train amachine learning model. The information may be an identity of a chemicalcomposition and an associated value of a property of the chemicalcomposition. The trained machine learning model may be used to determineand/or predict a value (e.g., an unknown value) of a chemicalcomposition (such as a considered chemical composition), for example,based on an identity of the chemical composition (e.g., the chemicalcomposition).

At 410, the machine learning model may be deployed. At deployment, themachine learning model may determine a value of a property of a chemicalcomposition (e.g., a considered chemical composition) based on anidentity of the chemical composition, may determine an identity of thechemical composition based on a value of a property of a chemicalcomposition, etc.

The determined value of the property of the chemical composition may becompared against a desired value of the property of the chemicalcomposition. For example, the pH value returned from the machinelearning model may be compared against a desired pH value. The pH valuereturned from the machine learning model may be compared against anactual (e.g., actually measured) pH value. If it is determined that thevalue of the property is the same (e.g., substantially the same) as thedesired value, the entity may move towards creating a chemicalcomposition (e.g., a personal care product) having the desired value ofthe property. The entity may use the ingredients input into the machinelearning model to create the chemical composition having the desiredvalue of the property. For example, the entity may create a chemicalcomposition using the ingredients input into the machine learning modelthat resulted in a determined (e.g., predicted) pH value that isdesired. A personal care product may be created using the chemicalcomposition such that the personal care product will be comprised ofingredients resulting in the desired value of the property.

FIG. 5 is a block diagram of an example system 500 for determining(e.g., predicting) data associated with a composition (e.g., chemicalcomposition) forming a product, such as a personal care product. Datamay include an identity of a chemical composition, a value of a propertyof a chemical composition, and/or one or more types of data. The datamay be determined based on one or more attributes and/or parameters. Forexample, system 500 may determine (e.g., predict) the data associatedwith a property of a chemical composition based on one or moreattributes/parameters and machine learning techniques. Although examplesprovided herein may relate to determining (e.g., predicting) an identityof a chemical composition, a value of a property of a chemicalcomposition, and/or a fitting parameter using machine learningtechniques, a person of skill in the art will understand that one ormore other values and/or parameters relating to a chemical compositionmay be determined (e.g., predicted) using machine learning techniques.For example, chemoinformatic values of ingredients of a chemicalcomposition may be determined, chemical constants may be determined,consumer perceptions may be determined, etc.

System 500 includes a user device 502 configured to connect to aproperties modeling device, such as example chemical properties modelingdevice 602 (further described in FIG. 6) via a network 520. Network 520may include wired and/or wireless communication networks. For example,networks 520 may include a local area network (LAN), a metropolitan areanetwork (MAN), and/or a wide area network (WAN). Network 520 mayfacilitate a connection to the Internet. In further examples, network520 may include wired telephone and cable hardware, satellite, cellularphone communication networks, etc.

User device 502 may include a user interface 504, a memory 506, acentral processing unit (CPU) 508, a graphics processing unit (GPU) 510,an image capturing device 514, and/or a display 512. User device 502 maybe implemented as a user equipment (UE) such as a mobile device, acomputer, laptop, tablet, desktop, or any other suitable type ofcomputing device.

User interface 504 may allow a user to interact with user device 502.For example, user interface 504 may include a user-input device such asan interactive portion of display 512 (e.g., a “soft” keyboard displayedon display 512), an external hardware keyboard configured to communicatewith user device 504 via a wired or a wireless connection (e.g., aBluetooth keyboard), an external mouse, or any other user-input device.The user interface 504 may allow a user to input, view, etc. one or morepieces of information relating to a chemical composition forming apersonal care product.

Memory 506 may store instructions executable on the CPU 508 and/or theGPU 510. The instructions may include machine readable instructionsthat, when executed by CPU 508 and/or GPU 510, cause the CPU 508 and/orGPU 510 to perform various acts. Memory 506 may store instructions thatwhen executed by CPU 508 and/or GPU 510 cause CPU 508 and/or GPU 510 toenable user interface 504 to interact with a user. For example,executable instructions may enable user interface to display (viaDisplay 512) one or more prompts to a user, and/or accept user input.Instructions stored in memory 506 may enable a user to input an identityof a chemical composition and/or a value of a property of the chemicalcomposition, for example. In other examples, a user may utilize userinterface 504 to click, hold, or drag a cursor to define identities,values, and/or properties of a chemical composition.

CPU 508 and/or GPU 510 may be configured to communicate with memory 506to store to and read data from memory 506. For example, memory 506 maybe a computer-readable non-transitory storage device that may includeany combination of volatile (e.g., random access memory (RAM)) ornon-volatile (e.g., battery-backed RAM, FLASH, etc.) memory.

Image capturing device 514 may be configured to capture an image. Theimage may be a two-dimensional image, a three-dimensional image, etc.Image capturing device 514 may be configured to capture an image in adigital format having a number of pixels. Although image capturingdevice 514 is illustrated in FIG. 5 as internal to user device 502, inother examples image capturing device 514 may be internal and/orexternal to user device 502. In an example, image capturing device 514may be implemented as a camera coupled to user device 502. Imagecapturing device 514 may be implemented as a webcam coupled to userdevice 502 and configured to communicate with user device 502. Imagecapturing device 514 may be implemented as a digital camera configuredto transfer digital images to user device 502 and/or to chemicalproperties modeling device 602. Such transfers may occur via a cable, awireless transmission, network 520/620, and/or a physical memory carddevice transfer (e.g., SD Card, Flash card, etc.), for example. Imagecapturing device 514 may be used to capture an image of a personal careproduct, a chemical composition forming the personal care product, datarelating to the chemical composition, data relating to one or morefeatures of a personal care product, etc.

In examples the user may input information into the user device 502relating to one or more compositions (e.g., chemical compositions). Thechemical composition information may be transferred to and/or from thechemical property modeling device 602, as shown in FIG. 5. With thechemical property modeling device 602 having information relating to thechemical compositions (e.g., the identities of the chemical compositionsand/or the values of the properties of the chemical compositions), thechemical property modeling device 602 may return information about thechemical composition. For example, the chemical property modeling device602 may provide values (e.g., predicted values) of properties ofchemical compositions.

User device 502 may obtain information (e.g., unknown information) aboutone or more chemical compositions (e.g., names of chemical compositions,ingredients of chemical compositions, chemoinformatic values ofingredients of chemical compositions, values of properties of chemicalcompositions, etc.) for prediction purposes. For example, a user (e.g.,a user of user device 502) may desire to know an identity of a chemicalcomposition having a value (e.g., desired value) of a property of apersonal care product. The value of the property may be affected by oneor more ingredients of the chemical composition interacting within oneanother. The value (e.g., desired value) of a property of a personalcare product may be a value (e.g., a pH value) of the personal careproduct, a function of one or more ingredients of the personal careproduct, a classification of one or more ingredients of the personalcare product, a consumer perception of the personal care product, etc.

The user may input one or more types and/or values of chemicalcomposition information (e.g., names, ingredients, chemoinformaticproperties, etc.) into the user device 502, for example, to determineinformation (e.g., other information) about the chemical compositions.The user device 502 may transmit the information to chemical propertymodeling device 602. In examples all or some of the steps, processes,methods, etc., may be performed by one device or more than one device(e.g., user device or chemical property modeling device). For example,user device 502 may include chemical properties engine 630 in examples.In other examples, chemical property modeling device 602 may be externalto user device 502.

In examples in which chemical property modeling device 602 is separatefrom user device 502, user device 502 may communicate with chemicalproperty modeling device 602 via one or more wired and/or wirelesstechniques, as described herein. For example, as shown in FIG. 5, userdevice 502 may communicate with chemical property modeling device 502via network 520. Network 520 may be the Internet, in some examples. Inother examples, as described herein, network 520 may be Wi-Fi,Bluetooth, LAN, etc.

A value of a property (e.g., a desired value of a property) of achemical composition may be received. The chemical composition in whichthe value of the property is received and in which the identity of thechemical composition is to be determined by machine learning rules maybe referred to as a considered chemical composition. For example, a usermay receive a value (e.g., a desired value) of a property of a chemicalcomposition. The user may transfer the value of the property to thechemical property modeling device 602. The value may relate to aproperty that is affected by one or more ingredients of the chemicalcomposition, such as a pH value, a fluoride stability value, a viscosityvalue, an abrasion value, a specific gravity value, a consumerperception value, etc.

Based on the value of the property, the chemical property modelingdevice 602 may provide an identity of a chemical composition that has(e.g., is predicted to have) that value (e.g., or approximately thatvalue) for the property. For example, the chemical property modelingdevice 602 may provide a name of a composition that has (e.g., ispredicted to have) the value, ingredients of a chemical composition thathas (e.g., is predicted to have) the value, chemoinformatic values ofingredients of a chemical composition that has (e.g., is predicted tohave) the value, etc.

The user may also, or alternatively, provide information related to thechemical composition to determine a value of a property of the chemicalcomposition. For example, the user may input into the chemical propertymodeling device 602 a name of a composition, ingredients of a chemicalcomposition, chemoinformatic values of ingredients of a chemicalcomposition, consumer perceptions of the chemical composition, etc.Based on the name, ingredient, and/or chemoinformatic information, thechemical property modeling device 602 may determine a value of aproperty of the chemical composition. For example, based on the name,ingredient, and/or chemoinformatic information, the chemical propertymodeling device 602 may determine a pH value, a fluoride stabilityvalue, a viscosity value, an abrasion value, a specific gravity value,etc., of the chemical composition.

FIG. 6 shows an example system 600 of training a properties engine, suchas chemical properties engine 630. Chemical properties engine 630 may behoused in chemical property modeling device 602, although such aconfiguration is for illustration purposes only. As shown in FIG. 6,training device 650 may communicate with chemical property modelingdevice 602. For example, training device 650 may communicate withchemical property modeling device 602 via network 620. One or moretraining devices 650 may provide information to the chemical propertymodeling device 602, for example, to train the chemical propertiesengine 630 of chemical property modeling device 602, as describedherein.

Training device 650 may provide information to a modeling device, suchas chemical property modeling device 602. Information provided tochemical property modeling device 602 may include experimentallymeasured information relating to a chemical composition (e.g., achemical composition forming a personal care product), mathematicallycalculated information relating to a chemical composition, consumerperception information relating to a chemical composition, etc. Trainingdevice 650 may provide information relating to chemical compositionsthat includes identities of chemical compositions (e.g., names ofchemical compositions, ingredients of chemical compositions,chemoinformatic values of ingredients of chemical compositions, etc.).The training device 650 may provide values of properties of chemicalcompositions, such as actual values of properties of chemicalcompositions and/or mathematically determined values of properties ofchemical compositions.

As provided herein, information provided by training device 650 may bebased on actual (e.g., actually measured information, such as values ofchemical compositions having actually been measured). In addition, oralternatively, information provided by training device 650 may be basedon values of the chemical compositions being determined usingmathematical calculations, such as thermodynamic calculations of thechemical compositions to determine values of properties of the chemicalcompositions. Providing this information (e.g., actual informationand/or thermodynamically calculated information) to the chemicalproperties engine 630 may be used to train the model, using machinelearning techniques, as described herein. The chemical composition forwhich information is used to train the machine learning rules may bereferred to as a sample chemical composition.

Chemical property modeling device 602 may include a CPU 608, memory 606,GPU 610, interface 616, and chemical properties engine 630. Memory 606may be configured to store instructions executable on the CPU 608 and/orthe GPU 610. The instructions may include machine readable instructionsthat, when executed by CPU 608 and/or GPU 610, cause the CPU 608 and/orGPU 610 to perform various acts. CPU 608 and/or GPU 610 may beconfigured to communicate with memory 606 to store to and read data frommemory 606. For example, memory 606 may be a computer-readablenon-transitory storage device that may include any combination ofvolatile (e.g., random access memory (RAM), or a non-volatile memory(e.g., battery-backed RAM, FLASH, etc.) memory.

Interface 616 may be configured to interface with one or more devicesinternal or external to chemical property modeling device 602. Forexample, interface 616 may be configured to interface with trainingdevice 650 and/or chemical properties database 624. Chemical propertiesdatabase 624 may store information about chemical compositions, such asnames of chemical compositions, ingredients of chemical compositions,chemoinformatic values of ingredients of chemical compositions, valuesof properties of chemical compositions (e.g., pH values, fluoride (e.g.,fluoride stability) values, viscosity (e.g., viscosity stability)values, abrasion (e.g., stain removal and dentin abrasion) values,specific gravity values, consumer perception (e.g., sweetness,stickiness, fragrance) values), etc. The information stored withinchemical properties database 624 may be used to train the chemicalproperties engine 630. The information stored within chemical propertiesdatabase 624 may also, or alternatively, be referenced by chemicalproperties engine 630 for determining (e.g., predicting) informationabout a chemical composition (e.g., a considered chemical composition).

A device (e.g., user device 502 and/or chemical property modeling device602) may receive information of one or more chemical compositions viatraining device 650 and/or another device. The information may relate toone or more (e.g., many) different types of chemical compositions, afamily of chemical compositions, complete and/or incomplete chemicalcompositions, chemical compositions with extensive history, relativelyunknown chemical compositions, etc.

One or more types of information of a chemical composition may beprovided to chemical property modeling device 602. For example, one ormore types of information of a chemical composition (e.g., samplechemical composition) may be provided to chemical property modelingdevice 602 to train the chemical property modeling device 602 (e.g.,machine learning rules of the chemical property modeling device 602).For example, for a (e.g., each) chemical composition, the chemicalproperty modeling device 602 may receive actual (e.g., actuallymeasured) information of the chemical composition, calculated (e.g.,thermodynamically calculated) information of the composition, predictedinformation of the chemical composition, identity information of thechemical composition, consumer preference information of the chemicalcomposition, etc. The chemical property modeling device 602 may performan association of the information so that a prediction of chemicalcomposition data (e.g., similar chemical composition data) may beperformed.

Chemical property modeling device 602 may use machine learningtechniques to develop a software application (e.g., a model). Forexample, chemical properties engine 630 may include machine learningrules for determining (e.g., predicting) information relating to achemical composition. Chemical properties engine 630 may include a model(e.g., a machine learning model) to determine (e.g., predict)information regarding a chemical composition. The information providedto the model and/or the information provided by the model may be used totrain the model. The information used to train the model may includeidentities (e.g., names, ingredients, chemoinformatic values ofingredients, etc.) of a chemical composition, values of properties ofthe chemical composition, consumer perception information of thechemical composition, etc. The information provided to and/or by themodel to train the model may relate to chemical compositions (e.g.,sample chemical compositions).

The chemical properties engine 630 may include currently known and/orlater developed machine learning rules or algorithms. The machinelearning rules may be supervised machine learning rules and/orunsupervised machine learning rules. For example, the chemicalproperties engine 630 may include at least one of a Random Forest rule,Support Vector Machine rule, Naïve Bayes Classification rule, Boostingrule, a variant of a Boosting rule, Alternating Decision Tree rules,Support Vector Machine rules, Perceptron rules, Winnow rules, Hedgerules, rules constructing a linear combination of features or datapoints, Decision Tree rules, Neural Network rules, logistic regressionrules, log linear model rules, Perceptron-like rules, Gaussian processrules, Bayesian techniques, probabilistic modeling techniques,regression trees, ranking rules, Kernel Methods, Margin based rules,linear/quadratic/convex/conic/semi-definite programming techniques, orany modifications of the foregoing.

The chemical properties engine 630 may improve its ability to perform atask as it analyzes more data related to the task. As described herein,the task may be to determine (e.g., predict) unknown informationrelating to a chemical composition forming a personal care product. Theunknown information may be an unknown value of a property of a chemicalcomposition, for example, from known information. For example, the taskmay be to predict the value of a property of a chemical compositionbased on identity information of the chemical composition. Conversely,the task may be to predict the identity of a chemical composition basedon a value of a property of the chemical composition. In such examples,the more information (relating to one or more chemical compositions)provided to the model, the better the results from the model may be. Forexample, the model may provide more accurate determinations of values ofproperties of chemical compositions based on the model receivingnumerous pieces of information of the chemical compositions andinformation related to the identities of the chemical compositions.

As described herein, the machine learning model may be trained using aset of training examples. Each training example may include an exampleof an object, along with a value for the otherwise unknown property ofthe object. By processing a set of training examples that include theobject and/or the property value for the object, the model may determine(e.g., learn) the attributes or characteristics of the object that areassociated with a particular property value. This learning may then beused to predict the property or to predict a classification for otherobjects. As described herein, machine learning techniques (e.g., rules,algorithms, etc.) may be used to develop models for one or more chemicalcompositions.

Chemical compositions (and/or one or more ingredients of the chemicalcompositions) forming a personal care product may be identified and/orclassified based on product, function, classification, consumerperception, etc. One or more chemical compositions (and/or one or moreingredients within the chemical compositions) may be identified and/orclassified prior to the chemical compositions being input into themachine learning rules. One or more chemical compositions (and/or one ormore ingredients within the chemical compositions) may be identifiedand/or classified by the machine learning rules. For example, machinelearning rules may identify and/or classify chemical compositions(and/or one or more ingredients within the chemical compositions) basedon product, function, classification, consumer perception, etc.

Models (e.g., machine learning models) may be developed to receiveinformation relating to a chemical composition, for example, todetermine (e.g., predict) information of a chemical composition.Training examples (e.g., training sets or training data) may be used totrain the chemical properties engine 630. For example, the training datamay include the names of sample chemical compositions, ingredients ofsample chemical compositions, chemoinformatic values of ingredients ofsample chemical compositions, fitting parameters of sample chemicalcompositions, functions of sample chemical compositions, classificationsof sample chemical compositions, values of properties of sample chemicalcompositions, etc. The values of properties of sample chemicalcompositions may be determined via calculations, such as viathermodynamic calculations. The values of properties of sample chemicalcompositions may be determined via an experimental measurement. Asdescribed herein, properties of sample chemical compositions may includethe pH, fluoride stability, viscosity stability, abrasion, specificgravity, consumer perception properties, etc. of the sample chemicalcomposition.

After training the chemical properties engine 630 (e.g., the machinelearning model of chemical properties engine 630) using training data,the chemical properties engine 630 may be used to determine (e.g.,predict) data. For example, the chemical properties engine 630 may beused to determine (e.g., predict) parameters that are similar to theparameters used to train the chemical properties engine 630. As anexample, chemical properties engine 630 may be trained using identities(e.g. ingredients) of chemical compositions and values of a pH propertyof chemical compositions. The chemical properties engine 630 may be usedto determine unknown values of a pH property, for example, based on theidentity (e.g., ingredients) of the chemical composition.

In other examples, after training the chemical properties engine 630(e.g., the machine learning model of chemical properties engine 630)using training data, the chemical properties engine 630 may be used todetermine parameters that are different than the parameters used totrain the chemical properties engine 630. As an example, chemicalproperties engine 630 may be trained using identities (e.g. ingredients)of chemical compositions and values of a pH property of chemicalcompositions. The chemical properties engine 630 may be used todetermine unknown values of a soluble zinc property. The chemicalproperties engine 630 may be used to determine unknown values of asoluble zinc property based on the identity (e.g., ingredients) of thechemical composition. The different parameters may have a relationshipwith one another. The relationship between the different parameters mayallow the chemical properties engine 630 to predict the differentparameters. Using the example above, although the pH property and thesoluble zinc property are different properties, there may be arelationship with the pH property and the soluble zinc property thatallows the chemical properties engine 630 to predict the soluble zincdata based on pH training data.

Other data related to a chemical composition may be used to train thechemical properties engine 630. For example, data related to a propertymay be used to train the chemical properties engine 630. As an example,the data related to the property may be a fitting parameter associatedwith the value of the property, although other types of data may be usedto train the chemical properties engine 630. As an example, trainingdata may include an identity (e.g., a name, ingredients, and/orchemoinformatic values of ingredients) of sample chemical compositionsand fitting parameters of sample chemical compositions.

A fitting parameter may be used to determine a value of a parameter at adefined instance. For example, the fitting parameter may relate to therate at which a value changes over time. The fitting parameter may beused to determine a value of a parameter at a future date, day, time,time period, etc. A fitting parameter may be used to define continuousfunctions. A fitting parameter may be used to determine a value of aproperty at one or more (e.g., any) point in time. For example, if avalue of fluoride stability has been measured at 4, 8, and 13 weeks, afitting parameter may be derived which may provide values (e.g.,expected values) of fluoride stability at intermediate timepointsbetween 4, 8, and 13 weeks and/or at extended points beyond 13 weeks.

Determining a value of a property at a future date, day, time, timeperiod, etc. may be useful as manufacturers of products (e.g., personalcare products) may be required to demonstrate that a product (e.g.,personal care product) maintains a minimum threshold quantity of aproperty throughout the shelf-life of the product. For example, as shelflives of a product may be on the order of several years, it may beimpractical to test products (e.g., new products) at certain timeperiods (e.g., months, years, etc.) to determine the viability of theproducts. It may be useful to collect data (e.g., collect data over ashort period of time) and use a fitting parameter to extrapolate thevalue of the property at a longer period of time. Such a model (e.g., amodel which predicts fitting parameters) may predict properties attimepoints for which there may be no experimental data.

As an example, a chemical properties engine 630 may be trained usingdata related to a chemical composition. As described herein, the datamay be an identity (e.g., ingredients) of the chemical composition aswell as other data. For example, the chemical properties engine 630 maybe trained using ingredients of chemical compositions, molecular weightsof the ingredients (e.g., each ingredient), weight percentages of theingredients (e.g., each ingredient), etc. The weight percentages of theingredients (e.g., each ingredient) may be converted to a molarconcentration. The chemical properties engine 630 may be trained usingmolar concentrations, theoretical total fluoride content, and/or solublefluoride after aging of a chemical composition (e.g., after aging of achemical composition for 13 weeks at 40 degrees Celsius). After trainingthe chemical properties engine 630 (e.g., the machine learning model ofchemical properties engine 630) using training data, the chemicalproperties engine 630 may be used to determine (e.g., predict) data. Forexample, the chemical properties engine 630 may be used to determine(e.g., predict) a value for soluble fluoride after aging, based on anidentity (e.g., ingredients) of a chemical composition and/or based onmolecular concentration data related to the chemical composition.

A fitting parameter may be used to determine a value of a parameter at afuture time period. For example, the chemical properties engine 630 maybe trained using ingredients of chemical compositions, molecular weightsof the ingredients (e.g., each ingredient), weight percentages of theingredients (e.g., each ingredient), and/or the fitting parameter. Aftertraining the chemical properties engine 630 (e.g., the machine learningmodel of chemical properties engine 630) using ingredients of chemicalcompositions, molecular weights of the ingredients (e.g., eachingredient), weight percentages of the ingredients (e.g., eachingredient), and/or the fitting parameter, the chemical propertiesengine 630 may be used to determine the fitting parameter. The fittingparameter may be used with a fitting function to determine a definedinstance, as described herein. For example, the chemical propertiesengine 630 may be used to determine (e.g., predict) a value of a fittingparameter that can be used with a fitting function to determine thetheoretical total fluoride content and/or the soluble fluoride contentmeasured after aging of a chemical composition (e.g., after aging of achemical composition at 4, 8, and/or 13 weeks at 40 degrees Celsius).Example fitting functions may include an exponential function,polynomial function, power function, trigonometric function, althoughother fitting functions may be used.

Information included in the training data may be selected and/or inputinto the model of chemical properties engine 630 based on a functionand/or classification. For example, a chemical composition and/or aningredient of a chemical composition may have a defined function and/orclassification, such as an ingredient in a chemical composition having abinding function, a preserving function, a whitening function, analcohol classification, an ethers classification, etc. The function mayrelate to how one or more of the ingredients of the chemical compositionare used in product form. The function may relate to a pellicle cleaningratio (PCR) and/or a relative dentin abrasivity (RDA). PCR is ameasurement of stain removal and may represent the cleaning efficacy ofa personal care product, such as a toothpaste. RDA is a measurement ofabrasivity (e.g., pure abrasivity) and may represent the erosivecapability of a personal care product, such as a toothpaste. Examplefunctions 700 of a chemical composition and/or an ingredient within thechemical composition may be found in FIG. 7. Example classifications 800of a chemical composition and/or an ingredient within the chemicalcomposition may be found in FIG. 8. Although FIG. 7 and FIG. 8 provide alist of functions and classifications, respectively, it should beunderstood by those of skill in the art that the functions provided inFIG. 7 and the classifications are provided in FIG. 8 are for examplepurposes only and are non-limiting.

In examples, one or more chemical compositions may (e.g., may only) beinput into a model if the chemical compositions have a function (e.g., adesired function). As an example, a set may consist of eighty chemicalcompositions. Of the eighty chemical compositions, fourteen chemicalcompositions may include an ingredient that provides a function ofwhitening. A user may desire to determine a value of a property of achemical composition wherein the chemical composition (e.g., aningredient of the chemical composition) may have a whitening function.In such an example, the model may be trained using (e.g., only using)chemical compositions (e.g., ingredients of the chemical composition)having a whitening function, such as the fourteen chemical compositionsin the above example. Also, or alternatively, the model may categorize(e.g., automatically categorize, dynamically categorize, etc.) chemicalcompositions based on the function of the chemical composition.

With the model including (e.g., only including) chemical compositions ofa defined function, or the model categorizing chemical compositionsbased on a defined function, the chemical properties engine 630 mayprovide information of a chemical composition having (e.g., only having)the function. For example, the chemical properties engine 630 maydetermine a value of a property of a chemical composition having afunction (e.g., a flavoring function, a binding function, etc.) based onan identity of the chemical composition. Conversely, the chemicalproperties engine 630 may determine an identity of a chemicalcomposition having a function based on a value of a property of thechemical composition.

In other examples, one or more chemical compositions may (e.g., mayonly) be input into a model if the chemical compositions have aclassification (e.g., a chemical classification). The classification mayrelate to molecular properties of an ingredient of a chemicalcomposition, such as a chemical composition forming a personal careproduct. The chemical compositions may (e.g., may only) be input into amodel if the chemical compositions have a desired classification.Example chemical classifications may include an alcohols classification,an amino acids classification, an enzymes classification, a fatty acidsclassification, a ketones classification, peptides classification, aswell as other classifications provided in FIG. 8.

As an example, a set may consist of forty chemical compositions. Of theforty chemical compositions, ten chemical compositions may include aningredient that is classified as an ether. A user may desire todetermine a value of a property of a chemical composition wherein thechemical composition (e.g., an ingredient of the chemical composition)may have a classification of an ether. In such an example, the model maybe trained using (e.g., only using) chemical compositions (e.g.,ingredients of the chemical composition) having a classification of anether. Also, or alternatively, the model may categorize (e.g.,automatically categorize, dynamically categorize, etc.) chemicalcompositions based on the classifications of the chemical composition.

With the model including (e.g., only including) chemical compositions ofa defined classification, or the model categorizing chemicalcompositions based on a defined classification, the chemical propertiesengine 630 may provide information of a chemical composition having(e.g., only having) the classification. For example, the chemicalproperties engine 630 may determine a value of a property of a chemicalcomposition having a classification (e.g., an alcohols classification, afatty acids classifications, etc.) based on an identity of the chemicalcomposition. Conversely, the chemical properties engine 630 maydetermine an identity of a chemical composition having a classificationbased on a value of a property of the chemical composition.

As described herein, information relating to one or more chemicalcompositions may be input into the model based on a function,classification, consumer perception, etc., of the chemical compositionand/or ingredient of the chemical composition. Information (e.g.,identities, values of properties, etc.) of chemical compositions may beidentified based on experimentation, simulation, mathematicalcomputations, analysis, clinical consumer trials, and/or assumptionsregarding the property being modeled. For example, actual (e.g. actuallymeasured) values of properties of chemical compositions may beidentified and input into the model.

A training set (e.g., identities of chemical compositions and associatedvalues of properties of the chemical compositions) may be used to traina machine learning model (e.g., chemical properties engine 630). Themachine learning model (e.g., chemical properties engine 630) mayperform a selected machine learning rule or algorithm using the trainingset, as described herein. Once trained, the model may be used todetermine (e.g., predict) the identity and/or values of properties ofthe chemical composition, relative to the property of interest.

FIG. 9A shows a block diagram of example data used for training chemicalproperties engine 630. Data 902 may relate to one or more chemicalcompositions (e.g., sample chemical compositions). Data 902 may be knownand/or determined. For example, data 902 may be known by experimentallymeasuring the data, mathematically calculating (e.g., via thermodynamiccalculations) the data, receiving the data from storage (e.g., from adatabase, such as a chemical properties database 624), receiving thedata from clinical trials, etc. Data 902 may include values for one ormore parameters. For example, data 902 may include identities ofchemical compositions. Identities may include the names of the chemicalcompositions, ingredients of the chemical compositions, chemoinformaticvalues/properties of the ingredients of the chemical compositions,values of properties of the chemical compositions, consumer perceptionsof the chemical compositions, etc. Data may be input into the chemicalproperties engine 630, for example, to train the model to predict one ormore values of chemical compositions.

As shown in FIG. 9A, data 902 may include one or more ingredients 912 ofone or more chemical compositions. Ingredients may include a firstingredient 912 a, a second ingredient 912 b, etc. Example ingredientsare provided in FIGS. 1A, 1B and 2A, 2B. For example, a chemicalcomposition may include water, glycerin, propylene glycol, and flavoringredients. In such example, data 902 may include data for water,glycerin, propylene glycol, and flavor ingredients. Each ingredient 912a, 912 b, etc., may include an identity of the ingredient and/or achemoinformatic value for the ingredient. For example, data 902 mayinclude chemoinformatic values 914 a, 914 b, etc. In the examplechemical composition that includes glycerin, propylene glycol, andflavor ingredients, each of glycerin, propylene glycol, and flavor willhave a respective chemoinformatic value within data 902.

Data 902 may include a value 904 of a property of the chemicalcomposition. The value 904 of the property may be affected by one ormore of the ingredients. For example, the value 904 of the property maybe affected by one or more of the ingredients interacting with one ormore other ingredients of the chemical composition. A property may bepH, fluoride stability, viscosity stability, abrasion, specific gravity,a consumer perception of the chemical composition, etc. Data 902 mayinclude a value of the property, such as a value of a pH property. Asdescribed herein, the value of the property (e.g., pH property) may beaffected by one or more of the ingredients of the chemical composition.

One or more values of data 902 may be input into the chemical propertiesengine 630, for example, to train the chemical properties engine 630.Identities of chemical compositions and associated values of properties(e.g., other properties) of the chemical compositions may be input intothe chemical properties engine 630. As an example, ingredients ofchemical composition (e.g., sample chemical composition) and associatedvalues of parameters may be input into the chemical properties engine630. The chemical properties engine 630 may provide an association ofthe ingredients of the chemical compositions and the values ofparameters of the chemical compositions.

FIG. 9B shows a block diagram of example data 920 used for determininginformation relating to a chemical composition using chemical propertiesengine 630. For example, FIG. 9B shows a block diagram of example data920 used for determining a value of a property via chemical propertiesengine 630. Data 920 may relate to one or more chemical compositions(e.g., considered chemical compositions). Data 920 may be known and/ordetermined. For example, data 920 may be known and/or determined byreceiving the data from storage (e.g., from a database, such as achemical properties database 624), experimentally measuring the data,mathematically calculating (e.g., via thermodynamic calculations) thedata, etc. Data 920 may include values for one or more parameters. Data920 may include identities of chemical compositions, such as the namesof the chemical compositions, ingredients of the chemical compositions,chemoinformatic values/properties of the ingredients of the chemicalcompositions, values of properties of the chemical compositions, etc.Data 920 may be input into the chemical properties engine 630, forexample, to determine from the chemical properties engine 630 (e.g.,machine learning model of chemical properties engine 630) one or morevalues of properties of chemical compositions.

Data 920 may include one or more ingredients 912 of one or more chemicalcompositions. Ingredients may include a first ingredient 912 a, a secondingredient 912 b, etc. For example, a chemical composition may includewater, glycerin, propylene glycol, and flavor ingredients. In suchexample, data 920 may include data for water, glycerin, propyleneglycol, and flavor ingredients. Each ingredient 912 a, 912 b, etc., mayinclude a chemoinformatic value for the ingredient. For example, data902 may include chemoinformatic values 914 a, 914 b, etc. In the examplechemical composition that includes glycerin, propylene glycol, andflavor ingredients, each of glycerin, propylene glycol, and flavor willhave a respective chemoinformatic value within data 920.

One or more values of data 902 may be input into the chemical propertiesengine 630, for example, to determine (e.g., determine from the chemicalproperties engine 630) a value of a property of the chemicalcomposition. For example, ingredients of a chemical composition (e.g., asample chemical composition) may be input into the chemical propertiesengine 630. The chemical properties engine 630 may run (e.g., process)one or more machine learning rules to determine a value of a property ofthe chemical composition. The chemical properties engine 630 may providethe value of the property of the chemical composition after determiningthe value.

Chemical properties engine 630 (e.g., model of chemical propertiesengine 630) may be configured to predict a value of a property of thechemical composition, for example, based on receiving identities ofingredients of a chemical composition, etc. In an example, the values ofthe properties of the chemical composition may be affected byingredients of the chemical composition (e.g., may be affected by aninteraction of one or more of the ingredients of the chemicalcomposition). When information relating to a chemical composition issupplied to the trained model, the output may comprise a predictionregarding the value of the property of the chemical composition, afitting parameter associated with the chemical composition, identitiesof the chemical composition, etc. The property may relate to a pH of thechemical composition, a viscosity stability of the chemical composition,an abrasion of the chemical composition, a specific gravity of thechemical composition, a consumer perception of the chemical composition,etc. The predictions may take the form of a value from a continuousrange of values or from a discrete value, for example.

FIG. 9C shows a block diagram of example data 930 used for determiningan identity of a chemical composition via chemical properties engine630. Data 930 may relate to one or more chemical compositions (e.g.,considered chemical compositions). Data 930 may be known. For example,as described herein, data 930 may be known by experimentally measuringthe data, mathematically calculating (e.g., via thermodynamiccalculations) the data, receiving the data via a clinical consumertrial, receiving the data from storage (e.g., from a database, such as achemical properties database 624), etc. As shown in FIG. 9C, data 930may include a value 904 of a property of a chemical composition, etc.Data 930 may be input into the chemical properties engine 630, forexample, to determine information (e.g., associated information) ofchemical compositions. For example, data 930 (e.g., value 904) may beinput into the chemical properties engine 630, for example, to determinefrom the model ingredients 912 of the chemical composition determined(e.g., predicted) to relate to the value 904 of the property input intothe chemical properties engine 630.

As described herein, data 930 may include a value 904 of a property. Thevalue 904 of the property may be input into chemical properties engine630, for example, to predict (e.g., determine) a name of a chemicalcomposition, one or more ingredients 912 of a chemical composition,chemoinformatic values 914 a, 914 b, . . . , 914 n of a chemicalcomposition, etc. Ingredients may include a first ingredient 912 a, asecond ingredient 912 b, etc. For example, a chemical composition mayinclude water, glycerin, propylene glycol, and flavor ingredients. Achemoinformatic value may be associated with an (e.g., each) ingredient912 a, 912 b, etc. For example, ingredient 912 a may includechemoinformatic value 914 a.

As an example, a value 904 of a property of the chemical composition maybe input into chemical properties engine 630. Based on value 904 of theproperty of the chemical composition, the chemical properties engine 630(e.g., model of chemical properties engine 630) may be configured topredict identities (e.g., names, ingredients, chemoinformatic values,etc.) of a chemical composition forming a personal care product. Wheninformation relating to a chemical composition is supplied to thechemical properties engine 630, the output may comprise a determination(e.g., prediction) regarding the identities (e g., names,chemoinformatic values, etc.) of a chemical composition forming thepersonal care product, a fitting parameter associated with the chemicalcomposition, a value (e.g., another value) of a property of the chemicalcomposition, etc. The chemical properties engine 630 may provide thenames, ingredients, chemoinformatic values, etc. of the chemicalcomposition to user via user device 502, for example.

FIGS. 10A-10C show example graphical user interfaces (GUIs) for traininga chemical properties modeling device 602 (e.g., a chemical propertiesengine 630 within chemical properties modeling device 602). The GUI maybe displayed on one or more devices. For example, the GUI may bedisplayed on a training device, such as training device 650, a userdevice, etc.

As shown in FIG. 10A, the GUI may request information from the user, forexample, the GUI may request information from the user via promptrequest 1010. Prompt request 1010 may ask the user what data the userwould like to use to train the chemical properties engine 630 (e.g.,model of chemical properties engine 630). The data used to train themodel may be referred to as sample data. The data may include anidentity (e.g., name, ingredients, chemoinformatic values ofingredients) of a chemical composition forming a personal care product,a value of a property of personal care product, etc. The GUI may providean input mechanism 1012 for the user to provide a response to promptrequest 1010. For example, the GUI may have a text box for receivingtext from the user, a radio button for selection, etc. As shown in FIG.10A, check box 1012 may be provided. In examples in which a text box isprovided, the user may check one or more of the data in the inputmechanism 1012 for training the chemical properties engine 630.

After the user selects the data desired to be input into the chemicalproperties engine 630 (e.g., for training the chemical properties engine630), the user may input such data. The user may input the data manually(e.g., via manually typing or speaking the data). The user may input asingle piece of data or the user may input multiple pieces of data. Forexample, as shown in FIG. 10B, GUI may provide an indication for theuser to select the data to be input into the chemical properties engine630. The GUI may display the data to be input into the chemicalproperties engine 630, For example, the GUI may display the data to beinput into the chemical properties engine 630 based on the inputprovided on input mechanism 1012 of FIG. 10A.

In an example, the user may desire to input ingredients 1016 a of achemical composition forming a personal care product and a value 1016 bof a property of the chemical composition. As shown in FIG. 10B, theuser may select a file to provide the ingredient information (via Browse1017 a) and the value (via Browse 1017 b) of the property information.Although FIG. 10B shows a Browse button for inputting data, one of skillin the art will understand that other methods exist for selecting and/orinputting data into chemical properties engine 630, such as via adatabase (such as a database housed on a server, such as a cloudserver), via one or more hard drives, via external devices (such as userdevice 502), etc.

The user may input data into the chemical properties engine 630 for oneor more chemical compositions. For example, the user may train thechemical properties engine 630 with data relating to tens, hundreds,thousands, etc., of chemical compositions. The user may train thechemical properties engine 630 with the same data for one or more of thechemical compositions. For example, the user may train the chemicalproperties engine 630 with ingredients and values of properties ofdozens of chemical compositions.

The user may train the chemical properties engine 630 with differentdata (e.g., types of data) for one or more of the chemical compositions.For example, the user may train the chemical properties engine 630 withingredients and values of properties for some of the chemicalcompositions, with chemoinformatic values and values of properties forsome of the chemical compositions, with names of chemical compositionsand values of properties for some of the chemical compositions, etc. Theuser may train the chemical properties engine 630 with values ofproperties comprising a pH, a fluoride stability, a viscosity stability,an abrasion, a specific gravity, consumer perceptions, etc. The trainingdevice 650 (e.g., GUI of training device) may request if the userdesires to input additional data. For example, as shown in FIG. 10C, theGUI may provide an additional data prompt 1018 asking the user if theuser desires to input any additional data into the chemical propertiesengine 630 (e.g., the model of chemical properties engine 630). The usermay desire to input additional data into chemical properties engine 630if the user desires to further train the chemical properties engine 630.If the user desires to input additional data into the chemicalproperties engine 630, the user may select the Yes prompt in area 1020,otherwise the user may select the No prompt in area 1020. If the userselects the Yes prompt in area 1020, the GUI shown in FIG. B (anddescribed herein) may be provided to the user. If the user selects theNo prompt in area 1020, the user may exit the GUI.

FIGS. 11A-11D show example graphical user interfaces (GUIs) fordetermining (e.g., predicting) data from a chemical properties modelingdevice 602 (e.g., a chemical properties engine 630 within chemicalproperties modeling device 602). The GUI may be displayed on one or moredevices. For example, the GUI may be displayed on a user device, such asuser device 502.

As shown in FIG. 11A, the GUI may request information from the user, forexample, via prompt request 1110. Prompt request 1110 may ask the userwhat data the user would like the model to determine (e.g., predict).The GUI may provide an input mechanism 1112 for the user to provide aresponse to prompt request 1110. For example, the GUI may have a textbox for receiving text from the user, a radio button for selection, etc.As shown in FIG. 11A, check box 1112 may be provided. The user may checkone or more of the data in the input mechanism 1112 so that the chemicalproperties engine 630 may determine one or more pieces of data relatingto the chemical composition. Input mechanism may permit additionalinformation, including sub-categories of information, to be selected fordetermination. For example, input mechanism 1112 may allow a user tofurther define the value of the property to be determined to be one apH, a fluoride stability, a viscosity stability, an abrasion, a specificgravity, a consumer perception, etc.

After the user selects what data the user desires the chemicalproperties engine 630 to determine, the user may input data that isassociated with the desired data, as shown in FIG. 11B. For example,prompt 1116 indicates that the user desires to determine a value of aproperty of the chemical composition (based on the user's input at input1112, in FIG. 11A). As shown in FIG. 11B, the GUI may provide input1118, allowing the user to select what data the user desires to inputinto the chemical properties engine 630, for example, to determine thevalue of the property of the chemical composition. Examples of data tobe input into the chemical properties engine 630 includes identity(e.g., name, ingredients, chemoinformatic values of ingredients) data ofa chemical composition forming a personal care product, a value of aproperty of personal care product, etc.

After the user selects the data that the user desires to determine(1112), and the data that the user would like to use to determine thevalue of the personal care product (1118), the user may provide theassociated data. FIG. 11C shows an example GUI in which user may atinput data, at 1122. Prompt 1120 indicates that the user has chosen toinput ingredients of the chemical composition (to determine a value of aproperty of the chemical composition), however such indication is forillustration purposes only and other types of data may be provided byuser to determine information relating to chemical composition.

The user may input the data manually (e.g., via manually typing orspeaking the data). For example, the user may input the ingredients ofthe chemical composition, as shown on FIG. 11C, manually. The user mayinput a single piece of data or the user may input multiple pieces ofdata. For example, as shown in FIG. 11C, GUI may provide an indicationfor the user to select the data to be input into the chemical propertiesengine 630. The user may select a file to provide the ingredientinformation (via Browse button 1122). Although FIG. 11C shows a Browsebutton 1122 for inputting data, one of skill in the art will understandthat other methods exist for selecting and/or inputting data intochemical properties engine 630, such as via a database (such as adatabase housed on the cloud), via external hard drives, via externaldevices (such as user device 502), etc.

The GUI may provide the determined (e.g., predicted) data. For example,the GUI may provide the value of a parameter, as shown in FIG. 11D. Thevalue of the parameter may relate to a chemical composition forming apersonal care product. Prompt 1130 may display the associated dataprovided by the user. For example, prompt 1130 may display that thedetermined data is based on the ingredient information (e.g., theingredient information provided by the user). Prompt 1130 may indicatewhat data has been determined. For example, prompt 1130 indicates thatthe value of the property of the personal care product is beingdetermined. Output 1132 provides the determined value. As shown on FIG.11D, the determined value may be 1.7. In examples the GUI may providefurther information of the information such as that the property is apH, a fluoride stability, a viscosity stability, an abrasion, a specificgravity, and/or a consumer perception.

FIG. 12 is an example process 1200 for determining (e.g., predicting) avalue of a chemical composition. The value may be an identity of achemical composition, such as a name of the chemical composition,ingredients of the chemical composition, chemoinformatic values ofingredients of the chemical composition, a value of a property of thechemical composition, etc.

At 1202, an identity of a chemical composition (e.g., a sample chemicalcomposition) may be received. The identity may be received from adatabase or another storage device, a described herein. As providedabove, the identity of the chemical composition may be a name of thechemical composition, ingredients of the chemical composition,chemoinformatic values of ingredients of the chemical composition, etc.

At 1204, a value of a parameter of the chemical composition (e.g., asample chemical composition) may be received. The value of the propertymay be affected by one or more ingredients of the chemical composition.As an example, the property may be a pH of the chemical composition, andthe value of the property may be the value of the pH of the chemicalcomposition.

The identity of the chemical composition (e.g., the sample chemicalcomposition) and/or the value of the parameter may be used to train amachine learning model, as described herein. For example, at 1206 thevalue of the property of the chemical composition (e.g., the samplechemical composition) may be input into the machine learning model totrain the machine learning model. An identity of the chemicalcomposition may be input into the machine learning model to train themachine learning model. The identity of the chemical composition may beone or more of a name of the chemical composition, ingredients of thechemical composition, chemoinformatic values of ingredients of thechemical composition, etc. The machine learning model may makeassociations of the value of the property of the chemical compositionand the identity of the chemical composition.

After the machine learning model is trained, the machine learning modelmay determine one or more values of a chemical composition. The machinelearning model may determine one or more values of a chemicalcomposition in response to receiving an associated piece of data. Forexample, the machine learning model may determine a value of a propertyof a chemical composition based on an identity of the chemicalcomposition, such as ingredients of the chemical composition or a nameof the chemical composition. The property of the chemical compositionmay be a pH value of the chemical composition, a fluoride stabilityvalue of the chemical composition, a viscosity value of the chemicalcomposition, an abrasion value of the chemical composition, a specificgravity value of the chemical composition, a consumer perception valueof a chemical composition, etc.

For example, at 1208 the machine learning model may receive one or morevalues of a chemical composition (e.g., a considered chemicalcomposition). As described herein, a considered chemical composition maybe a chemical composition in which one or more values are unknown anddesired to be known. For example, an identity of a considered chemicalcomposition may be known, ingredients of the considered chemicalcomposition may be known, and/or chemoinformatic values of theconsidered chemical composition may be known. The value of a property ofthe considered chemical composition may be unknown.

The known values (e.g., the identity of the considered chemicalcomposition, ingredients of the considered chemical composition, and/orchemoinformatic values of the considered chemical composition) may beinput into the machine learning model. Based on the values input intothe machine learning model, the machine learning model may determine avalue of a property of the considered chemical composition. The value ofthe considered chemical composition may be displayed or otherwiseprovided to a user.

In examples, the user may determine whether the value of the propertycorresponds to a desired value of the property. For example, it may bedesired (e.g., required) that a personal care product have a definedvalue for a property of the personal care product. The value may relateto a pH value or one or more other properties described herein. If themachine learning model determines that a chemical composition has avalue of the property that aligns with a desired value of the property,the user may perform an action, such as producing a personal careproduct having the ingredients associated with the determined value. Inother examples, the user may perform actions to confirm that the resultsprovided by the machine learning model are accurate, such as byperforming a measurement of the value of the property, performingmathematically calculations of the value. The user may confirm that theresults provided by the machine learning model are accurate beforeproducing a personal care product having the ingredients associated withthe determined value.

Systems described herein may be implemented using any available computersystem and adaptations contemplated for known and later developedcomputing platforms and hardware. Further, the methods described hereinmay be carried out by software applications configured to execute oncomputer systems ranging from single-user workstations, client servernetworks, large distributed systems employing peer-to-peer techniques,or clustered grid systems. In an example, a high-speed computing clustermay be used. The computer systems used to practice the methods describedherein may be geographically dispersed across local or nationalboundaries using a data communications network such as the Internet.Moreover, predictions generated at one location may be transported toother locations using well known data storage and transmissiontechniques, and predictions may be verified experimentally at the otherlocations.

While the invention has been described with respect to specific examplesincluding presently preferred modes of carrying out the invention, thoseskilled in the art will appreciate that there are numerous variationsand permutations of the above described systems and techniques. It is tobe understood that other embodiments may be utilized and structural andfunctional modifications may be made without departing from the scope ofthe present invention. Thus, the spirit and scope of the inventionshould be construed broadly as set forth in the appended claims.

What is claimed is:
 1. A computer-implemented method of determining avalue of a property of a considered chemical composition, the methodcomprising: inputting, into a model, an identity of the consideredchemical composition, wherein the considered chemical compositioncomprises ingredients, each of the ingredients of the consideredchemical composition being associated with a value of a chemoinformaticproperty of chemoinformatic properties of the considered chemicalcomposition; wherein the model is trained by receiving an identity of asample chemical composition and a value of a property of the samplechemical composition, the value of the property of the sample chemicalcomposition being identified via at least one of an experimentalmeasurement of the sample chemical composition or a thermodynamiccalculation of the sample chemical composition; determining, via themodel, a value of the property of the considered chemical compositionbased on the identity of the considered chemical composition, whereinthe value of the property of the considered chemical composition isaffected by an interaction of at least two of the ingredients of theconsidered chemical composition; and producing a personal care productcomprised of the considered chemical composition; wherein the inputtingand determining steps are performed by one or more processors.
 2. Themethod according to claim 1, wherein the property of the consideredchemical composition comprises at least one of a pH, a rheology, anabrasivity, a chemical degradation, a phase change, a turbidity, aningredient solubility, a volatile loss, or a consumer perception.
 3. Themethod according to claim 1, wherein the identity of the consideredchemical composition comprises identities of the ingredients of theconsidered chemical composition.
 4. The method according to claim 1,wherein the model is a machine learning model and the value of theproperty of the considered chemical composition is determined via rulesof the machine learning model.
 5. The method according to claim 4,wherein the machine learning model comprises a supervised learningapproach.
 6. The method according to claim 1, wherein the property ofthe considered chemical composition relates to a physiochemical propertyof the considered chemical composition, the physiochemical propertyrelating to a physical property or a chemical property of the consideredchemical composition.
 7. The method according to claim 1, wherein theproperty of the considered chemical composition is identical to aproperty of the sample chemical composition used to train the model. 8.The method according to claim 1, wherein the personal care productcomprised of the considered chemical composition is a toothpaste.
 9. Themethod according to claim 1, wherein the chemoinformatic property of theconsidered chemical composition relates to at least one of a qualitativecategory, a qualitative sensory attribute, a molecular formula, an aciddissociation constant, a solubility product, a structural topology, afunctional group count, a chemical fragment count, a hydrophobicity, apartition coefficient, a steric parameter, an association constant, or ahydrophile lipophile balance (HLB).
 10. The method according to claim 9,wherein the qualitative category comprises at least one of an ingredientfunction or an ingredient classification.
 11. A computer-implementedmethod of determining a fitting parameter value of a considered chemicalcomposition, the method comprising: inputting, into a machine learningmodel, at least one of an identity of the considered chemicalcomposition or values of chemoinformatic properties of ingredients ofthe considered chemical composition, wherein each of the ingredients ofthe considered chemical composition is associated with a value of achemoinformatic property of chemoinformatic properties of the consideredchemical composition; wherein the machine learning model is trained byreceiving an identity of a sample chemical composition and a value of afitting parameter associated with a value of a property of the samplechemical composition, the value of the property of the sample chemicalcomposition being affected by an interaction of at least two of theingredients of the sample chemical composition; determining, via themachine learning model, the fitting parameter value of the consideredchemical composition based on at least one of an identity of theconsidered chemical composition or values of chemoinformatic propertiesof ingredients of the considered chemical composition, wherein thefitting parameter value of the considered chemical composition isassociated with a value of a property of a considered chemicalcomposition, the property of the considered chemical composition beingaffected by an interaction of at least two of the ingredients of theconsidered chemical composition; and producing a personal care productcomprised of the considered chemical composition; wherein the inputtingand determining steps are performed by one or more processors.
 12. Themethod according to claim 11, further comprising: determining the valueof the property of the considered chemical composition at an instancebased on (1) an identity of the instance, (2) the fitting parametervalue of the considered chemical composition, and (3) a fittingfunction.
 13. The method according to claim 11, wherein the instance isa future time period.
 14. The method according to claim 12, wherein thefitting function comprises at least one of an exponential function,polynomial function, power function, or trigonometric function.
 15. Acomputer-implemented method of determining an identity of a consideredchemical composition, the method comprising: inputting, into a model, atleast one of values of chemoinformatic properties of ingredients of theconsidered chemical composition or a value of a property of theconsidered chemical composition, wherein the value of the property ofthe considered chemical composition is affected by an interaction of atleast two of the ingredients of the considered chemical composition;wherein the model is trained by receiving values of chemoinformaticproperties of ingredients of a sample chemical composition and a valueof a property of the sample chemical composition; determining, via themodel, the identity of the considered chemical composition based on theat least one of the values of the chemoinformatic properties of theingredients of the considered chemical composition or the value of theproperty of the considered chemical composition; and producing apersonal care product comprised of the identified considered chemicalcomposition; wherein the inputting and determining steps are performedby one or more processors.
 16. The method of claim 15, wherein thedetermining of the identity of the considered chemical composition isbased on the value of the property of the considered chemicalcomposition and not based on the values of the chemoinformaticproperties of the ingredients of the considered chemical composition.17. The method of claim 15, wherein each of the ingredients of theconsidered chemical composition has a respective chemoinformaticproperty.
 18. The method according to claim 17, wherein thechemoinformatic property of each of the ingredients of the consideredchemical composition relates to at least one of a qualitative category,a qualitative sensory attribute, a molecular formula, an aciddissociation constant, a solubility product, a structural topology, afunctional group count, a chemical fragment count, a hydrophobicity, apartition coefficient, a steric parameter, an association constant, or ahydrophile lipophile balance (HLB).
 19. The method of claim 15, whereinthe model is a machine learning model relating to a supervised learningapproach.
 20. The method of claim 15, wherein the personal care productcomprised of the considered chemical composition is a toothpaste.